Efficient implementation of the genetic algorithm to solve rich vehicle routing problems

• Published in: Operational research Vol. 21; no. 3; pp. 1763 - 1791
• Main Authors: Rabbouch, Bochra, Saâdaoui, Foued, Mraihi, Rafaa
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2021; Springer Nature B.V
• Subjects: Business and Management; Combinatorial analysis; Computational Intelligence; Constraints; Genetic algorithms; Logistics; Management Science; Operations Research; Operations Research/Decision Theory; Optimization; Original Paper; Route planning; Vehicle routing; Vehicles; Windows (intervals); Time windows; Heterogeneous limited fleet; Genetic algorithm; Multi depot; Rich VRP; MDHVRPTW; VRPLIB; Combinatorial optimization problem
• ISSN: 1109-2858; 1866-1505
• DOI: 10.1007/s12351-019-00521-0

The aim of this paper is to further study the rich vehicle routing problem (RVRP), which is a well-known combinatorial optimization problem arising in many transportation and logistics settings. This problem is known to be subject to a number of real life constraints, such as the number and capacity limitation of vehicles, time constraints including ready and due dates for each customer, heterogeneous vehicle fleets and different warehouses for vehicles. A Genetic Algorithm (GA)-based approach is proposed to tackle this highly constrained problem. The proposed approach efficiently resolves the problem despite its high complexity. To the best of our knowledge, no GA have been used for solving multi-depot heterogeneous limited fleet VRP with time windows so far. The new algorithm has been tested on benchmark and real-world instances. In fact, promising computational results have shown its good cost-effectiveness.